Facilitating growth investment decisions

ABSTRACT

A technique for performing securable market analysis involves establishing an empirically-derived structure and evaluating market size using analytical techniques within that structure. Inputs to a system that incorporates the technique can include a functional job and related emotional and consumption jobs, if any; importance levels; satisfaction levels; job executors; and willingness-to-pay.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Applications61/310,268 filed Mar. 3, 2010, which is incorporated by reference.

BACKGROUND

Growth investments in new products and services fail frequently perhapsin part due to the application of traditional market definitions. Marketsizing is normally from the perspective of the company making a product.Traditional market sizing techniques try to quantify a marketopportunity by using products, technologies, and users as inputs intothe calculations. This is a flaw. Total addressable market (TAM) andserviceable addressable market (SAM) are two traditional marketdefinitions that inherently include the flaw.

Products are merely point-in-time solutions that help customers get jobsdone. The jobs do not change over time, but solutions do. For example,the job of storing music has been solved with pen and paper, pianorolls, Victrolas, LPs, 8-tracks, reel-to-reel tape, cassettes, CDs, andMP3 players. The job is constant, but the solutions change over time.The traditional market definitions are flawed because they use avariable (the product) rather than a constant (the job). In order tomanipulate data structures in an automated market sizing system, thedata structures must, of course, include pertinent data values.Moreover, where the values are inherently flawed, increasing thecomplexity of a market sizing system would have a tendency to increasethe flaws in a manner that would be non-transparent to a human user ofthe system.

The foregoing examples of the related art and limitations relatedtherewith are intended to be illustrative and not exclusive. Otherlimitations of the related art will become apparent upon a reading ofthe specification and a study of the drawings.

SUMMARY

In various examples, one or more of the above-described problems havebeen reduced or eliminated, while other examples are directed to otherimprovements. The following examples and aspects thereof are describedand illustrated in conjunction with systems, tools, and methods that aremeant to be exemplary and illustrative, not limiting in scope.

A technique for performing securable market analysis involvesestablishing an empirically-derived structure and evaluating market sizeusing analytical techniques within that structure. Inputs to a systemthat incorporates the technique can include a functional job and relatedemotional and consumption jobs, if any; importance levels; satisfactionlevels; job executors; and willingness-to-pay (WTP). Ideally, thestructure and tools facilitate a market sizing analysis that may not bepossible otherwise.

Securable market advantages include accurate sizing of markets based onwell-defined underserved customer needs. So securable market analysiscan be used to accurately quantify both existing market and new marketopportunities. And because securable market analysis is based on jobsand outcomes rather than specific product offerings, the inputs arestable, measurable, and accurate.

Securable market advantages include lower investment risk. Securablemarket analysis can be used before any investment in product developmentto lower the risk of launching the wrong product into an attractivemarket or launching a new product into an unattractive market.

Securable market advantages include accurate pricing. Securable marketanalysis enables management teams to analyze different price pointsbased on WTP to get a job done better. It also reveals price points thatwould cause customers to switch to a different solution.

Securable market advantages include accurate segmentation. Securablemarket analysis reveals which segments of a market should be targeted.The calculations are based on job executors who are underserved and whowould be willing to pay for a new solution that gets a job done better.Securable market segments are the target markets that should be pursued.Targeting other segments means a company is targeting customers who arealready satisfied with their ability to get a job done.

Securable market advantages include strategic applicability. Securablemarket analysis is applicable to every type of growth strategy: newmarket growth (targeting a job for which there are no market solutions),sustaining growth (getting a job done better with an existing product),adjacent market growth (getting a related job done better), ordisruptive growth (getting a job done worse with a low cost product).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example of a system for facilitating investment in asecurable market.

FIG. 2 depicts a graphical representation of a need curve.

FIG. 3 depicts a flowchart of an example of a method for facilitatinginvestment in a securable market.

FIG. 4 depicts a graphical representation of total revenue that could begenerated from new products or services that help customers get a jobdone at current satisfaction levels.

FIG. 5 depicts a graphical representation of a need curve in associationwith disruption market size.

FIG. 6 depicts an example of a job data structure.

FIG. 7 depicts an example of an outcome data structure.

FIG. 8 depicts an example of a solution data structure.

FIG. 9 depicts an example of a job data structure with substructures.

FIG. 10 depicts a flowchart of an example of a method for identifying asecurable market.

DETAILED DESCRIPTION

In the following description, several specific details are presented toprovide a thorough understanding. One skilled in the relevant art willrecognize, however, that the concepts and techniques disclosed hereincan be practiced without one or more of the specific details, or incombination with other components, etc. In other instances, well-knownimplementations or operations are not shown or described in detail toavoid obscuring aspects of various examples disclosed herein.

FIG. 1 depicts an example of a system 100 for facilitating investment ina securable market. The system 100 includes a network 101, an outcomemanagement engine 102, a market quantification engine 104, a securablemarket innovation engine 106, a growth investment engine 108, a jobsdatastore 110, a functional outcomes datastore 112, an emotionaloutcomes datastore 114, a consumption outcomes datastore 116, a marketsolutions datastore 118, and a securable solutions datastore 120.

In the example of FIG. 1, the network 101 can include a networked systemthat includes several computer systems coupled together, such as a localarea network (LAN), the Internet, or some other networked system. Theterm “Internet” as used in this paper refers to a network of networksthat uses certain protocols, such as the TCP/IP protocol, and possiblyother protocols such as the hypertext transfer protocol (HTTP) forhypertext markup language (HTML) documents that make up the World WideWeb (the web). Content is often provided by content servers, which arereferred to as being “on” the Internet. A web server, which is one typeof content server, is typically at least one computer system whichoperates as a server computer system and is configured to operate withthe protocols of the World Wide Web and is coupled to the Internet.Applicable known or convenient physical connections of the Internet andthe protocols and communication procedures of the Internet and the webare and/or can be used. The network 120 can broadly include, asunderstood from relevant context, anything from a minimalist coupling ofthe components illustrated in the example of FIG. 1, to every componentof the Internet and networks coupled to the Internet. However,components that are outside of the control of the system 100 can beconsidered sources of data received in an applicable known or convenientmanner.

A computer system will usually include a processor, memory, non-volatilestorage, and an interface. Peripheral devices can also be consideredpart of the computer system. A typical computer system will include atleast a processor, memory, and a device (e.g., a bus) coupling thememory to the processor. The processor can include, for example, ageneral-purpose central processing unit (CPU), such as a microprocessor,or a special-purpose processor, such as a microcontroller. The memorycan include, by way of example but not limitation, random access memory(RAM), such as dynamic RAM (DRAM) and static RAM (SRAM). The memory canbe local, remote, or distributed. The term “computer-readable storagemedium” is intended to include physical media, such as memory.

The bus can couple the processor to non-volatile storage. Thenon-volatile storage is often a magnetic floppy or hard disk, amagnetic-optical disk, an optical disk, a read-only memory (ROM), suchas a CD-ROM, EPROM, or EEPROM, a magnetic or optical card, or anotherform of storage for large amounts of data. Some of this data is oftenwritten, by a direct memory access process, into memory during executionof software on the computer system. The non-volatile storage can belocal, remote, or distributed. The non-volatile storage is optionalbecause systems can be created with all applicable data available inmemory.

Software is typically stored in the non-volatile storage. Indeed, forlarge programs, it may not even be possible to store the entire programin memory. Nevertheless, it should be understood that for software torun, if necessary, it is moved to a computer-readable locationappropriate for processing, and for illustrative purposes, that locationis referred to as the memory in this paper. Even when software is movedto the memory for execution, the processor will typically make use ofhardware registers to store values associated with the software, andlocal cache that, ideally, serves to speed up execution. As used herein,a software program is assumed to be stored at any known or convenientlocation (from non-volatile storage to hardware registers) when thesoftware program is referred to as “implemented in a computer-readablestorage medium.” A processor is considered to be “configured to executea program” when at least one value associated with the program is storedin a register readable by the processor.

The bus can also couple the processor to one or more interfaces. Theinterface can include one or more of a modem or network interface. Itwill be appreciated that a modem or network interface can be consideredto be part of the computer system. The interface can include an analogmodem, isdn modem, cable modem, token ring interface, satellitetransmission interface (e.g. “direct PC”), or other interfaces forcoupling a computer system to other computer systems. The interface caninclude one or more input and/or output (I/O) devices. The I/O devicescan include, by way of example but not limitation, a keyboard, a mouseor other pointing device, disk drives, printers, a scanner, and otherI/O devices, including a display device. The display device can include,by way of example but not limitation, a cathode ray tube (CRT), liquidcrystal display (LCD), or some other applicable known or convenientdisplay device.

In one example of operation, the computer system can be controlled byoperating system software that includes a file management system, suchas a disk operating system. One example of operating system softwarewith associated file management system software is the family ofoperating systems known as Windows® from Microsoft Corporation ofRedmond, Wash., and their associated file management systems. Anotherexample of operating system software with its associated file managementsystem software is the Linux operating system and its associated filemanagement system. The file management system is typically stored in thenon-volatile storage and causes the processor to execute the variousacts required by the operating system to input and output data and tostore data in the memory, including storing files on the non-volatilestorage.

Some portions of the detailed description may be presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs to configure the general purposesystems in a specific manner in accordance with the teachings herein asspecifically purposed computer systems, or it may prove convenient toconstruct specialized apparatus to perform the methods of someembodiments. The required structure for a variety of these systems willappear from the description below. In addition, the techniques are notdescribed with reference to any particular programming language, andvarious embodiments may thus be implemented using a variety ofprogramming languages.

Referring once again to the example of FIG. 1, the outcome managementengine 102 is coupled to the jobs datastore 110, functional outcomesdatastore 112, the emotional outcomes datastore 114, and the consumptionoutcomes datastore 116. As used in this paper, an engine includes adedicated or shared processor and, typically, firmware or softwaremodules that are executed by the processor. Depending uponimplementation-specific or other considerations, an engine can becentralized or its functionality distributed. An engine can includespecial purpose hardware, firmware, or software embodied in acomputer-readable medium for execution by the processor. As used in thispaper, a computer-readable medium is intended to include all mediumsthat are statutory (e.g., in the United States, under 35 U.S.C. 101),and to specifically exclude all mediums that are non-statutory in natureto the extent that the exclusion is necessary for a claim that includesthe computer-readable medium to be valid. Known statutorycomputer-readable mediums include hardware (e.g., registers, randomaccess memory (RAM), non-volatile (NV) storage, to name a few), but mayor may not be limited to hardware.

A datastore can be implemented, for example, as software embodied in aphysical computer-readable medium on a general- or specific-purposemachine, in firmware, in hardware, in a combination thereof, or in anapplicable known or convenient device or system. Datastores in thispaper are intended to include any organization of data, includingtables, comma-separated values (CSV) files, traditional databases (e.g.,SQL), or other applicable known or convenient organizational formats.Datastore-associated components, such as database interfaces, can beconsidered “part of” a datastore, part of some other system component,or a combination thereof, though the physical location and othercharacteristics of datastore-associated components is not critical foran understanding of the techniques described in this paper.

Datastores can include data structures. As used in this paper, a datastructure is associated with a particular way of storing and organizingdata in a computer so that it can be used efficiently within a givencontext. Data structures are generally based on the ability of acomputer to fetch and store data at any place in its memory, specifiedby an address, a bit string that can be itself stored in memory andmanipulated by the program. Thus some data structures are based oncomputing the addresses of data items with arithmetic operations; whileother data structures are based on storing addresses of data itemswithin the structure itself. Many data structures use both principles,sometimes combined in non-trivial ways. The implementation of a datastructure usually entails writing a set of procedures that create andmanipulate instances of that structure.

The job datastore 110 includes job data structures associated with jobsthat a customer needs to get done. Jobs are independent of products, andcould be referred to as “product-independent jobs.” Products are merelypoint-in-time solutions that help customers get jobs done. The jobs donot change over time, but the solutions do. For example, the job ofstoring music has been solved with pen and paper, piano rolls,Victrolas, LPs, 8-tracks, reel-to-reel tape, cassettes, CDs, and MP3players. It may be noted that the existence of a job may be constrainedby the technology of the time. For example, sequencing DNA, when it wasnew, was a job, but there were not tools to do it. At the time, the newmarket could have been identified even though there were no products orservices to sequence DNA. As another example, the market for findingone's car was a zero-dollar solution (write parking space on a piece ofpaper) until a solution (use GPS) was provided. Since jobs are constant,the job data structures stored in the job datastore 110 are not uniquelyassociated with specific products, which generally change over time,though the inspiration to identify a job could come, at leasttheoretically, from any source.

A job can be characterized as the goal or task for which a solution isneeded, and can include problems that need to be solved. For example,orthopedic surgeons need to replace a joint, carpenters need to make astraight cut, and IT managers need to manage software licenses. Marketscan be defined based on jobs because the job is what a customer wants toaccomplish, regardless of available solutions. When customers determinethat they have a job to do, they look for a product or service that theycan “hire” to help get the job done. If a new product is available thathelps get the job done better, customers may “fire” the old product and“hire” the new one. For example, when MP3 players were introduced,consumers hired them at a rapid rate because they helped get the job ofstoring music done better than CDs. And when the stent was introduced,it was quickly adopted because it helped open an artery better than anangioplasty balloon alone.

Every job follows a job map, which is a series of steps that must becompleted to execute the job successfully. Most jobs consist of 8-12steps. (Emotional and consumption jobs frequently have fewer steps.) Forexample, in order to make a straight cut, a carpenter must plan the cut,adjust the tool, ensure safety, start the cut, operate the tool, monitorthe cut, modify the cut, and finish the cut. The job map ensures thatall of the steps are captured because it reveals when the job starts andends.

The functional outcomes datastore 112 includes outcome data structures.For every job data structure in the jobs datastore 110, there is atleast one outcome data structure in the functional outcomes datastore112 that is associated with each step. Most steps consist of 6-12outcomes. The outcomes are defined independent of a solution, and theyare structured with a vector of improvement for an object of control.The vector of improvement can include a metric (e.g., time, likelihood,number, etc.) and direction of improvement (e.g., minimize, increase,etc.). The job data structure, including the outcomes at each step inthe job map, enables the precise and unambiguous quantification of whata customer will need to accomplish in order to execute the jobsuccessfully. For example, in order to communicate in a dangeroussituation, firefighters need to minimize the time it takes to confirmreceipt of a communication. In order to make a straight cut, carpentersneed to increase the likelihood that the blade will begin cuttingprecisely on the cut line. And in order to open an artery, cardiologistsneed to minimize the number of side vessels that are inadvertentlyentered.

The emotional outcomes datastore 114 includes outcome data structuresthat are similar to the outcome data structures of the functionaloutcomes datastore 112. Emotional jobs (and emotional outcomes) areassociated with and support a functional job. Customers frequently tryto get emotional jobs done as well as functional ones. For example, ITmanagers want to be perceived by their organization as having madesignificant process improvements. And musicians want to feel that theycan perform new ideas without interruption. Emotional jobs are morelikely than functional jobs to include fewer steps (e.g., one step) andfewer outcomes per step (e.g., one outcome). Emotional jobs could beindirectly related to specific functional jobs (e.g., the emotional jobcould be associated with a position within an organization regardless offunctional jobs that are being suggested for the organization), directlyrelated to a functional job (e.g., the emotional job could be associatedwith one or more surgical jobs), or associated with a functional outcome(e.g., the emotional job could be associated with outcomes that have todo with safety). There are advantages in using similar data structuresfor both functional and emotional outcomes.

The consumption outcomes datastore 116 includes outcome data structuresthat are similar to the outcome data structures of the functionaloutcomes datastore 112. Consumption jobs (and consumption outcomes) areassociated with and support a functional job. Customers frequently haveto expend resources to prepare to accomplish a job. For example, apurchase decision maker may need to consider the resources consumed byinstalling, learning to use, interfacing with, maintaining, etc. asolution associated with a functional job. Consumption jobs are morelikely than functional jobs to include fewer steps (e.g., one step) andfewer outcomes per step (e.g., one outcome). Consumption jobs could beindirectly related to specific functional jobs (e.g., the consumptionjob could be associated with a position within an organizationregardless of functional jobs that are being suggested for theorganization), directly related to a functional job (e.g., theconsumption job could be associated with one or more surgical jobs), orassociated with a functional outcome (e.g., the consumption job could beassociated with outcomes that have to do with safety). There areadvantages in using similar data structures for both functional andconsumption outcomes.

Since there are normally 8-12 steps and 6-12 outcomes per step, mostfunctional jobs include 48-144 different functional outcomes, plusconsumption and/or emotional outcomes. This reveals that markets aremore complex than traditionally thought. The identification of jobsdebunks the widespread myth that customers have latent needs. Customersknow what jobs they are trying to get done and how to measure success.With the right techniques, it is possible to uncover all customer needs(even those that may be unarticulated) in any market, even those inwhich no products exist. It is also possible to accurately size marketopportunities, including new market opportunities, if sufficientlysophisticated tools are developed to take advantage of theseobservations.

As described in this paper, it is assumed that the jobs datastore 110 isrelatively static. That is, jobs have already been identified andentered in the relevant format and since jobs do not change over timethere is theoretically no need to update them. Of course, perfection israrely achieved in practice. The outcome management engine 102 couldfacilitate the addition of new job data structures when new jobs areidentified, the modification or deletion of existing job data structures(presumably because the job data structure was flawed in some way, sincejobs are relatively static), or otherwise manage data in the jobsdatastore 110.

In addition, jobs can have an associated importance that varies overtime and an associated “willingness to pay” (WTP) that also varies overtime. WTP for a job or outcome represents the amount that a customerwould be willing to pay for a job or outcome that is executed with“perfect” satisfaction. In one implementation, WTP can be referred to as“maximum WTP” because it represents the maximum price a job executorwould pay for a solution that is 100% satisfactory. Where a distinctionis intended to be drawn, WTP derived in association with jobs oroutcomes can be referred to as “outcome-driven WTP” or simply “WTP”whereas WTP derived in association with specific products could bereferred to as “product-specific WTP” or “traditional WTP.”

Traditional techniques use hypothetical responses from customers todetermine a hypothetical WTP in association with a product or selectionof products. A problem with traditional WTP is that it is subject touncertainty and bias, which tends to reduce accuracy. In order todevelop a more accurate WTP, a technique should address issues ofperformance uncertainty, preference uncertainty, hypothetical responsebias, strategic response bias, and purchase control divergence. Usingrigorously defined jobs and outcomes facilitates the development of atechnique to reduce uncertainty and bias in WTP.

Using a range for WTP (or reservation price) is effective at increasingthe accuracy of direct elicitation surveys. The technique is known asICERANGE (for incentive-compatible elicitation of a consumer'sreservation price range), and it builds on techniques that have loweredthe likelihood of hypothetical and strategic bias. With the ICERANGEtechnique, respondents can be rewarded for telling the truth andpenalized for lying, and respondents are made aware that there is nolink between their stated WTP and the price they may need to pay. Theproblem with the ICERANGE technique is that it involves analysis of aproduct. By analyzing jobs and outcomes instead of products,hypothetical and strategic bias can be reduced by making it even clearerto respondents that their WTP is not associated with an actual productprice.

When determining WTP, it may be desirable to establish a WTP range froma maximum price, A, to an indifference price, B, to a ceiling price, C,for each survey respondent. To graphically illustrate a need curve, linesegments associated with each survey respondent can be placed on a gridas illustrated in the example of FIG. 2. A line drawn through the linesegments ABC can be referred to as the need curve 202. The need curve202 is the range of WTP corresponding to the number of underserved jobexecutors. The need curve 202 is an accurate representation of the pricecustomers will pay to get a job done better. It reflects marketopportunity because it segments customers into different pricecategories and enables sizing of those segments. It should be noted thatthe need curve 202 is similar to a traditional demand curve, plottingprice against quantity, but the need curve 202 is associated with WTPand number of underserved job executors.

In the example of FIG. 2, the line segments 204, 206 are representativeof the number of job executors with a WTP in the indicated range. Sincethe line segment 204 is higher along the WTP axis than the line segment206 and lower along the Underserved Job Executors axis, the line segment204 is associated with a higher WTP for which fewer job executors wouldpay relative to the lower WTP for which more job executors would pay ofline segment 206.

Depending upon context, it may be noted that WTP could be indicative ofthe willingness-to-pay of a specific job executor. Thus, context couldvary the meaning of WTP. For example, with reference to FIG. 2, arespondent's WTP can be associated with a maximum price for which therespondent would purchase a solution (A), a ceiling price above which arespondent would never go (C), some value in between (B), or a range(e.g., A-C). For illustrative clarity, henceforth when referring to WTPin this paper, WTP shall be indicative of the willingness-to-pay of jobexecutors considered in the aggregate (by combining the individualscores in a convenient manner), unless context dictates otherwise.

Preference and performance uncertainty issues arise in traditionalelicitation surveys because customers are asked about their WTP as itrelates to a specific product. Customers have preference uncertaintybecause when a product is being analyzed, the customer has to figure outon their own if the product's features address the right outcomes.Without explicitly stating which outcome the feature is supposed toaddress, customers will have a relatively high degree of uncertaintyabout the product's benefits. Customers have performance uncertaintybecause when a product is being analyzed, the customer has to figure outhow well the product's features satisfy the outcomes. Without explicitlystating which outcome a features is supposed to address, customers haveto come to their own conclusions about the satisfaction levels they canexpect the product to achieve while executing the job. Preference andperformance uncertainty exist because traditional techniques are unableto clearly state for the customer what the job is that the product issupposed to help execute and what the outcomes are that the product'sfeatures are supposed to satisfy.

In order to solve this problem and reduce both preference andperformance uncertainty, customers can be asked to analyze ajob—including associated consumption jobs and emotional jobs, ifapplicable—and its outcomes. Including associated consumption jobs andemotional jobs facilitates development of a quantitative metric oneverything a customer needs to accomplish in order to get the job done,and improves the accuracy of WTP. Including associated emotional jobscan tap into non-functional needs of the job executor. When the job isquantified and outcomes are explicitly shown to a customer, uncertaintycan be reduced. To accomplish this goal, job data structures and outcomedata structures should have rigorous structural requirements. Theoutcome data structures described in this paper include a vector ofimprovement (including a direction of improvement and a metric ofimprovement) and an object of control, which is sufficiently rigorous toenable precise and unambiguous quantification of what customers need toaccomplish to execute a job successfully, in practice resulting inreduced performance and preference uncertainty.

In consumer markets, the job executor is frequently both the purchasedecision maker and the job beneficiary. In business markets, the jobexecutor is often far removed from the purchase decision maker, whichmeans there is relatively large purchase control divergence. A productis purchased to execute a functional job, but the product must alsosatisfy consumption jobs (e.g., purchase, install, learn-to-use,maintain, etc.). In situations where there is a large purchase controldivergence, the purchase decision maker is executing one or more of theconsumption jobs, including the purchase job. In order to mitigate therisk that purchase control divergence leads to inaccurate WTP analysis,the population of consumption job executors as well as functional jobexecutors can be sampled, which can include analysis of the degree towhich the job executor has influence over the purchase decision maker.By sampling both the job executor and the purchase decision maker, WTPis more accurate; by revealing the functional job to the purchasedecision maker, the decision maker can also have a better understandingof the true execution costs and risks involved. Also, by sampling thepurchase decision maker, it is possible to derive a quantitative valuefor the constraints that might limit WTP, such as budgetary orregulatory constraints.

Referring once again to the example of FIG. 1, as described in thispaper, it is assumed that the functional outcomes datastore 112, theemotional outcomes datastore 114, and the consumption outcomes datastore116 are relatively static. Outcomes can have associated importance andWTP and the importance and WTP of jobs can be computed by aggregatingthe importance and WTP of outcomes that make up the jobs, potentiallymodified by the job or job type. For example, the importance of aparticular outcome that is identical in a first and second job may havegreater importance and/or associated WTP in the first job than in thesecond job. For illustrative simplicity, the importance and WTP aregenerally described in this paper in direct association with theoutcomes data structures and are indirectly described in associationwith the jobs data structures; the importance and WTP of the jobs aretreated as an aggregate of the importance and WTP of the outcomes thatmake up the job, with the understanding that a job could modify theimportance and/or WTP of an outcome in a manner that is in some wayjob-dependent.

The market quantification engine 104 can access the jobs datastore 110,the functional outcomes datastore 112, the emotional outcomes datastore114, and the consumption outcomes datastore 116. In addition, the marketquantification engine 104 can add, modify, delete, or otherwise managedata in the market solutions datastore 118. Advantageously, the marketquantification engine 104 can be running to continuously identifymarkets using potentially changing data points. As data points change,the market quantification engine 104 can calculate attractive marketsfor investment. Since the market is, as used in this paper, a(product-independent) job and a job executor, it is possible to identifymarkets for investment that do not even exist. Markets that do existwill have market solutions (e.g., products and services) thataccomplish, with varying levels of satisfaction, jobs for job executors.To obtain the market data, it is likely that the securable marketdetermination engine will be coupled to the network 101 and obtain datafrom data sources (not shown) coupled to the network 101.

The market quantification engine 104 can store market solution datastructures in the market solutions datastore 118. Market solutions areproducts and services that enable job executors to get jobs done or getthem done better (e.g., faster, cheaper, etc.). Market solution datastructures are augmented in practice by collecting other relevant data.For example, after a target market is identified, it may be advantageousto identify customers in a customer chain who may potentially beinvolved with any aspect of the job (e.g., raw material provider, partssupplier, manufacturer, OEM, distributor, retailer, service provider,purchase decision-maker, job executer, job beneficiary, andeducation/training), particularly those customers that are most likelyto be unsatisfied and/or on whom success depends. It is useful to knowthe frequency jobs/outcomes are performed, the general satisfaction withexisting solutions, the cost of existing solutions (both in terms ofpayment to hire the solution and in terms of overhead).

The outcome management engine 102 can work in conjunction with themarket quantification engine 104 to augment the jobs datastore 110, thefunctional outcomes datastore 112, the emotional outcomes datastore 114,and/or the consumption outcomes datastore 116 when the marketquantification engine 104 obtains new market data. For example, themarket quantification engine 104 can capture desired outcomes forrelevant jobs in a target market. The market quantification engine 104can consider a market (a job), the number of job executors and theirvarious outcomes, and the WTP for a new solution, and the outcomemanagement engine 102 can update, e.g., the functional outcomesdatastore 112 accordingly. A target market can be defined as a dollarestimate that is related to the number of job executors times the numberof job executors' WTP. The market quantification engine 104 can use ajob map for each job to assist in the analysis required to identifyoutcomes. The market quantification engine 104 can also perform orprompt for quantitative market research to capture importance andsatisfaction levels customers place on each outcome, which the outcomemanagement engine 102 can use to update, e.g., the functional outcomesdatastore 112. This can improve the quality of the data available to thesecurable market innovation engine 106.

The securable market innovation engine 106 analyzes relevant data in thejobs datastore 110, the functional outcomes datastore 112, the emotionaloutcomes datastore 114, and the market solutions datastore 118 togenerate at least one securable market solution data structure. This caninclude identifying where a market is under- and over-served and/oridentifying segments of opportunity. The securable market innovationengine 106 can calculate cost target ranges, as well as timing targetsusing individual and portfolio factors.

The securable market innovation engine 106 can store securable marketsolution data structures in the market solutions datastore 118.Advantageously, the securable market solution data structures can havesubstantially similar formats relative to the market solutions datastructures in the market solutions datastore 118. This enablescomparisons between current market solutions and market solutions thatdo not yet exist. Non-existent market solutions can generally becharacterized as targets for innovation, of which securable solutionsare a subset. In addition, each securable solution data structure canhave an associated outcome premium, either as part of the datastructure, stored in association therewith, or determinable using dataassociated with the data structure. There may also be a switching costassociated with each market solution data structures in general, butmost interestingly with each securable solution data structure (becausethe switching costs may be an issue for consumers switching from acurrent market solution to the securable solution). Switching costs canvary depending upon consumer-specific, current solution-specific, orother factors. For example, if a consumer is tech savvy and the newsolution requires some tech savvy, the switching cost may be lower thanfor a consumer that is not tech savvy and for which training time isincreased. As another example, if a consumer has a computer networkinfrastructure and the new solution requires the use of a computernetwork, the switching cost may be lower than for a consumer that doesnot have adequate computer system infrastructure.

The securable market innovation engine 106 can, if implemented to enableit, run automatically to compare market solutions to job and functionaloutcome metrics to compute securable solution data structures.Regardless of whether the engine automatically generates securablesolution data structures, the growth investment engine 108 facilitatesthe selection of securable solutions that have an associated positivepremium. Conceptually, the securable solution data structures can beconsidered part of the securable solutions datastore 120. The selectedsecurable solutions can be used to make decisions to invest in thedevelopment of a product with the intention of the product matching thesecurable solution data structure.

FIG. 3 depicts a flowchart 300 of an example of a method forfacilitating investment in a securable market. This flowchart and otherflowcharts are depicted in the figures of this paper as seriallyarranged modules. However, modules of the flowcharts may be reordered orarranged for parallel execution as appropriate.

In the example of FIG. 3, the flowchart 300 starts at module 302 withgenerating point-in-time market solution-independent data structures. Ina specific implementation, the module 302 could be accomplished by anoutcome management engine, such as is described with reference toFIG. 1. Generating data structures can entail receiving data(potentially including data in a data structure that is identical to thedata structure to be generated, which could also be referred to as“receiving market solution-independent data structures”) and storing thedata as a data structure in a datastore. Market solution-independentdata structures include job data structures. Each job data structure isassociated with a task to be accomplished, a goal to be reached, aproblem to be solved, or the like. Job data structures can includefunctional job data structures, consumption job data structures, andemotional job data structures.

Each job data structure is relatively static in the sense that jobs areidentified independently of any solutions that are currently availableto accomplish the job. Of course, it may be desirable to consider asubset of job types, which could entail deleting job data structuresthat have been established, but in this paper it is generally assumedthat once a job data structure is generated, it is not deleted even ifit is irrelevant in a particular context. Also, if new jobs arequantified, data structures associated with the new jobs can be added tothe existing set of job data structures, but those new jobs then becomerelatively static, as well. It should be noted that extant job datastructures can be modified, though ideally job data structures do nothave to be modified (thus, the term “static” is intended to represent atheoretical ideal, but in practice will likely simply represent the factthat the static data structures are point-in-time marketsolution-independent).

Jobs may or may not be divided into steps, each step including one ormore outcomes. Due to the relative complexity of job data structures,human operators may find it useful to have the dozens of outcomesdivided into steps to enable an abstraction of the process in somesituations. It may also be considered useful to have values of outcomes(such as importance, WTP, etc.) modified by similar values that areassociated with a step. That is, a particular outcome that is part of afirst step may have greater importance than the same outcome that ispart of a second step. Since steps are not essential to understandingthe techniques described in this paper, steps can be considered animplementation choice that will be largely ignored in this paper, infavor of the use of job data structures and the outcome data structuresthat are associated with the job data structures.

Jobs have an associated job map that includes one or more outcomes. So,conceptually, a job is different from an outcome even if the job has asingle outcome, at least because the job has an associated job map. Asingle-outcome job may or may not be identical to an outcome, but forillustrative purposes, jobs, which include a job map, are not outcomes,which do not. Accordingly, a job data structure includes a job map datastructure that identifies outcomes (and optionally steps) and an outcomedata structure does not include a job map data structure.

Outcomes can have multiple associated values, including importance, jobexecutors, and WTP, and can be defined in association with an object ofcontrol and vector of improvement (a metric and direction ofimprovement). Although outcomes can be defined independent of availablesolutions, market analysis can lead to changes in values associated withoutcomes, such as importance. Importance can be associated with anoutcome; outcome data structures can include an importance quantity.However, outcomes in different jobs may or may not have differentimportance levels; so each outcome data structure can have a differentassociated importance quantity, depending upon the context. Also,outcomes may have different importance levels to different individuals(e.g., a job executor and a purchase decision-maker); so the importanceassociated with a job data structure could be an aggregate of differentparties involved in execution of a job, in addition to being anaggregate of multiple different job executors. Importance can beobtained through interaction with current job executors, but importanceis ideally relatively constant across all market solutions. That is, theimportance of an outcome of a job should be the same for all marketsolutions for the job within a given context.

It is valuable to determine the number of job executors of a particularjob to estimate market size. An accurate count of job executors can bereplaced with an estimate, particularly if the number of job executorsis known to be higher than some threshold profitability value or if thejob does not yet have any market solutions, and it is often the casethat some estimation of the number of job executors is needed. Each jobexecutor has an associated job frequency, which is the number of timesthe job executor performs a job in a unit of time. In an alternative,job executor data could be completely replaced with job frequencies,though it is expected that maintaining a job executor data structure (asopposed to only a job frequency data structure) will be useful in otheraspects of market sizing and implementation of solutions. So jobexecutor data structures will generally include a job frequency value aswell as an association (e.g., a link) to the job executed by the jobexecutor, plus an association (e.g., a link) to the market solutionutilized to accomplish the job, if any. The number of job executors ismarket solution-independent, but may be discovered when performingmarket analysis. That is, market analysis may determine that jobexecutors are attempting to accomplish jobs using availablepoint-in-time market solutions, but the number of job executors isrelatively static.

WTP can be associated with a job and, for example, can be associatedwith outcomes and aggregated in a convenient fashion to yield a WTP inassociation with the job. It is possible to at least determine valueswithin a reasonable range of certainty using applicable known orconvenient techniques to obtain data associated with markets. WTP forperfect satisfaction (“perfect WTP”) can be obtained through interactionwith current job executors, and represents an ideal: the WTP for asolution that delivers perfect satisfaction. For individuals, perfectWTP can vary, but the various values can be aggregated to yield aperfect WTP for a particular job or outcome, or represented as a rangeof values.

In the example of FIG. 3, the flowchart 300 continues to module 304 withgenerating market solution-dependent data structures. In a specificimplementation, the module 304 could be accomplished by a marketquantification engine, such as is described with reference to FIG. 1.Market solution-dependent data structures can be derived from marketsolution-independent data structures and market data.

WTP for a market solution, as opposed to “perfect WTP,” is the WTP for asolution that helps accomplish a job at a particular satisfaction level.Thus, WTP equals perfect WTP times satisfaction, where satisfaction is avalue that ranges from 0 (completely unsatisfactory) to 1 (perfectlysatisfied). It may be noted that the formula shows that highersatisfaction is associated with higher WTP, but the relative monetaryamounts of the WTP will not necessarily be accurately represented bythis linear formula. It may be noted that the actual numeric values ofsatisfaction and other quantities described in this paper can be changedin any applicable convenient way. For example, satisfaction could have avalue that ranges from 0 to 10, 1 to 100, or some other applicable rangeof values. Different market solutions could have different satisfactionlevels per outcome that in the aggregate result in similar satisfactionlevels for the different market solutions. So it can be useful to tracksatisfaction levels on an outcome-by-outcome basis for the marketsolution, and it may also be useful to aggregate satisfaction levels formarket solutions as a whole or for steps within the market solution jobmap.

It may be noted that even though WTP is solution-dependent, it ispossible to obtain WTP (for some hypothetical or predicted satisfactionlevel) for a market solution that does not exist; a solution datastructure, including a satisfaction value, may or may not be associatedwith an actual product. A solution that is not associated with an actualproduct could be referred to as an “ad hoc” solution. Consider, forexample, the job of finding your car after you park it somewhere. An adhoc solution was, of course, to remember where you parked your car.Before certain technological innovations, such as GPS, the ad hocsolution was the only solution (an assumption made solely for thepurpose of this example, since a pen and paper could be used, as well).For the purposes of this paper, the ad hoc solution is treated as marketsolution dependent even if the ad hoc solution is not a product.

In this paper satisfaction, like WTP, is treated as a value associatedwith an outcome of a market solution (i.e., it is solution-specific)even if the market solution does not exist, and importance, like perfectWTP, is treated as a value associated with an outcome of a job (i.e., itis solution-independent). Every solution data structure is associatedwith a job data structure; so every solution data structure will alsohave an associated importance.

Cost can include multiple buckets. There are costs associated withcurrent payments for a market solution, costs associated with currentoverhead (e.g., resource consumption associated with using the currentmarket solution), and switching costs (e.g., resource consumptionassociated with switching to a target solution). It may be useful tokeep the costs categorized, and the costs could be even more finelycategorized (e.g., the switching cost could be divided into thesub-categories setup cost, installation cost, training cost, time toswitch, required infrastructure investment, etc.). Various costs can berepresented as consumption jobs. In general, costs are associated withmarket solutions, but, as is generally assumed in this paper, costs canbe associated with market solutions that do not exist.

Derived market solution-dependent quantities can includedissatisfaction, which can be represented as d=1−s, where 0≦d≦1 isdissatisfaction and 0≦s≦1 is satisfaction. More complex derivationsinclude opportunity: O=i+max(i−s,0), where O is the opportunity score,0≦i≦1 is importance, and 0≦s≦1 is satisfaction. Importance is associatedwith a job outcome data structure, as opposed to a market solutionoutcome data structure. Satisfaction is associated with a marketsolution outcome data structure, as opposed to a job outcome datastructure. The opportunity score represents a potential growthinvestment for a target solution that replaces or competes with acurrent market solution. The opportunity score is an accuraterepresentation of where a market is underserved, appropriately served,and over-served. An underserved market has relatively high importanceand relatively low satisfaction levels, whereas an over-served markethas relatively low importance and relatively high satisfaction levels.

A market solution can be offered as a job solution, as opposed to anoutcome solution. Thus, it can be desirable to have a way to representthe Opportunity Score of the job as a whole. An Opportunity Score for anoutcome of a job can be weighted by dividing the Opportunity Score ofthe outcome by the sum of all of the Opportunity Scores for outcomes ofthe job. As used in this paper, the Opportunity Score for the job is thesum of Weighted Opportunity Scores associated with the outcomes of thejob. Advantageously, by tweaking outcome Opportunity Scores, multipletarget solution data structures can be generated, and their weightedOpportunity Scores analyzed, in a manner that is not possible usingtraditional market valuation techniques.

Another relatively complex derivation is finding a securable population.The derivation is complex relative to traditional market sizingtechniques that use only existing market solutions and users of thosesolutions (plus the debunked myth of latent needs), and may even beimpossible using traditional market sizing techniques. In this paper,p=j*i*d, where p is the securable population, j is the number of jobexecutors, 0≦i≦1 is the importance, and 0≦d≦1 is dissatisfaction. TheSecurable Population accurately reflects the segment of job executorswho would accept more value in the market because they want to get a jobdone better.

Individual gross WTP for a job executor is defined as the job frequencyfor the job executor times the perfect WTP for the job. It may be notedthat the individual gross WTP is based on relatively static values (thejob), rather than on a particular market solution. That is, individualgross WTP equals [Job Frequency] times [perfect WTP], where both JobFrequency and perfect WTP are solution-independent. It may be useful toconsider Gross WTP, which is the sum of Individual Gross WTP for a job,in order to find a useful baseline for the market segment as a whole.

Individual Net WTP equals [Job Frequency] times [WTP]. WTP issolution-dependent because it is dependent on satisfaction with a marketsolution. So Individual Net WTP is also solution-dependent. It may benoted that the abstract value that Individual Net WTP represents can beassociated with the actual cost of the market solution used to obtainthe Individual Net WTP. The cost can include the amount the job executorpays for the market solution, and ideally should include overheadassociated with utilizing the market solution. The sum of the IndividualNet WTP for a job yields the Net WTP for a job using available marketsolutions.

In the example of FIG. 3, the flowchart 300 continues to module 306 withgenerating target solution data structures. (Optionally, current marketsolutions can also be mapped to market solution data structures, whichcan be useful for comparisons.) In a specific implementation, the module306 could be accomplished by a securable market innovation engine, suchas is described with reference to FIG. 1. Advantageously, usingtechniques described in this paper, it becomes possible to generateunique, accurate target solutions that do not exist on the market, andgenerate them with great speed. So multiple alternative target solutionscan be created for comparison with one another even though none of thetarget solutions exist (though one could also choose to generate onlyone). A useful quantity of a target solution data structure is targetWTP, which is a subset of WTP. As was mentioned previously, WTP is theamount a job executor is willing to pay for a market solution thataccomplishes a job with a certain level of satisfaction, and perfect WTPis a potentially theoretical market solution that accomplishes a jobwith perfect satisfaction. Target WTP is willingness to pay for asolution that does not exist, which can be considered a potential growthinvestment.

Target solution data structures can include data that varies dependingupon implementation. The values will generally at least include: theassociated functional job and associated emotional and consumptionjob(s), if any; importance levels; satisfaction levels; job executors;and WTP. As was discussed previously, some values may be directlyassociated with the target solution data structure (e.g., satisfactionand WTP), while others may be associated with the target solution datastructure because the target solution data structure is associated witha job data structure (e.g., importance and job executors). Relevantcosts can be treated as consumption jobs and relevant outcomes areincluded in a job map of the job data structure with which the targetsolution data structure is associated.

Target solution data structures can also have an outcome premium thatrepresents the difference between the target WTP and the WTP for someother solution. That is, p=W*(T−M), where p is the outcome premium, W isthe WTP, T is the target solution satisfaction and M is the marketsolution satisfaction. This formula is applicable to both functional andemotional outcomes. In another implementation, the formula is applicableto functional, emotional, and consumption outcomes. An outcome premiumcan also be generated for a functional job much as the OpportunityScore, mentioned above, is weighted for a job and/or for an emotionaljob. Where a distinction is intended to be drawn between functional andemotional premiums, the combination can be referred to as a totalpremium. That is, PβF+E, where P is the total premium, F is thefunctional outcome premium, and E is the emotional outcome premium.Advantageously, the outcome premium is accurate relative to a particularmarket solution regardless of the target solution, since the targetsolution satisfaction level can be selected (or a full range of targetsolution satisfaction levels can be considered in serial or parallel).Plus, a target solution data structure can have the same format asmarket solution data structures.

It may be noted that in a traditional market solution innovationprocess, an innovator will come up with a solution and then brainstormmore options. After brainstorming to come up with all of the options,the solution set is reduced in an arbitrary manner to obtain the finaltarget solution. Advantageously, with the approach described in thispaper, market solution-independent data structures can first begenerated to provide a target job, market solution-dependent datastructures can be generated to provide the current solutions to the job,and the data structures can be reduced to one or a manageable number ofvaluable target data structures having data structures identical to thecurrent market solution data structures and easily comparable to the jobdata structure that was generated at the outset.

In the example of FIG. 3, the flowchart 300 continues to module 308 withidentifying a securable market. In a specific implementation, the module308 could be accomplished by a securable market innovation engine, suchas is described with reference to FIG. 1. A securable market is thepecuniary volume that could be captured if a company helps customers geta job done better. It is a function of the percentage of the totalmarket that represents an opportunity from the perspective of thecustomer and independent of any solution. Securable market analysis ismore accurate than traditional analysis because the inputs arewell-defined, knowable, and quantifiable. Advantageously, securablemarket analysis enables a company to analyze the size of an opportunitybefore investing any capital in product development.

To understand securable markets, it is useful to understand “the needcurve.” For the population of job executors, the net WTP will range fromlow to high, which can be plotted (adjusted for switching costs) on whatis defined in this paper as the need curve. This is similar to theclassic economics demand curve, but is instead of plotting price andquantity, WTP and the number of underserved job executors is plotted.(As is mentioned above, the underserved job executors can be determinedas an opportunity score, which is a function of the importance of a joband the satisfaction with a market solution.) The size of an opportunityis dependent on the cost of getting the job done with alternativesolutions. The need curve reveals the price level below which jobexecutors would switch to a different solution at current satisfactionlevels.

FIG. 4 depicts a graphical representation 400 of total revenue thatcould be generated from new products or services that help customers geta job done at current satisfaction levels. The representation 400illustrates a securable market 402 in the area under the need curve 404at the current market 406. The current market 406 is associated with apoint on the need curve 404 that corresponds to a net WTP value andnumber of underserved job executors. The current market 406 isrepresentative of the cost of getting a job done today. The size of themarket can be grown by increasing satisfaction levels, as is representedby the increased satisfaction curve 408. The premium market 410 growswith the increased satisfaction curve over the securable market 402.(The low cost market size 412 is also depicted.) If a company candeliver a solution that increases satisfaction levels, the need curveshifts up and the market grows because job executors have a higher WTP(the premium) to get a job done quicker, with more stability, with morepredictable results, etc. In other words, the size of the marketopportunity cannot be increased by generating awareness, as traditionalmarket demand function and latent need beliefs suggest.

Because job and outcome satisfaction levels for market solutions areaccurately measurable, companies can use securable market analysis toanalyze whether there is a bigger opportunity to grow by developing apremium product that gets a job done better. The securable marketenables management teams to determine how to grow the market mostefficiently in order to generate high returns with relatively low risk.Securable market analysis can also be used to analyze growth throughdisruption, i.e., developing a product that gets a job done worse thanexisting solutions but at lower cost.

FIG. 5 depicts a graphical representation 500 of a need curve inassociation with disruption market size. FIG. 5 is similar to FIG. 4,and also includes a current market 506, but instead of an increasedsatisfaction curve 408, FIG. 5 depicts a decreased satisfaction curve508. The disruption market 514 grows from providing a solution thatresults in less satisfaction, but accomplishes a job at lower cost. Thesize of the disruption market 514 can be calculated using lower pricepoints with lower satisfaction levels than currently exist.

Referring once again to the example of FIG. 3, the flowchart 300continues to module 310 with determining expected market share for thetarget solution in the securable market. In a specific implementation,the module 310 could be accomplished by a securable market innovationengine, such as is described with reference to FIG. 1. Generally, jobexecutors can use alternatives to get a job done, including competingand ad hoc solutions. So in order to accurately project potential marketshare, it is useful to try to analyze all of the competition in themarket. A company can capture market share if its product adds morevalue for the job executor than competitive offers or alternativesolutions.

Advantageously, unlike traditional market analysis, value add, A, caninclude a weighted average improvement in outcome satisfaction levels.Specifically, A=(V_(new)−V_(old))/V_(old), where A is the added value ofthe new solution relative to the old solution, V_(new) is the valuescore of a new solution, and V_(old) is the value score of an oldsolution. The value, V, can be defined in association with a job havingn outcomes as:

${V = {\sum\limits_{i = 1}^{n}{S_{i}\frac{I_{i} + {\max \left( {{I_{i} - S_{i}},0} \right)}}{{\sum\limits_{j = 1}^{n}I_{j}} + {\max \left( {{I_{j} - S_{j}},0} \right)}}}}},$

where I_(k) and S_(k) are respectively the importance and satisfactionassociated with an outcome k of the n outcomes of the job. Note that thenumerator of the fraction is the opportunity score of an outcome, i, andthe denominator of the fraction is the sum of the outcome opportunityscores for the solution.

The value scores provide a baseline assessment of a securable market. Inother words, it can be determined with precision how much moresatisfaction a new product has to deliver in order to capture leadingmarket share. Every competitive product can be quantified relative tothe baseline value scores of each outcome. Specifically, a value, V_(i),for each outcome of a solution can be represented as:

$V_{i} = {S_{i}{\frac{I_{i} + {\max \left( {{I_{i} - S_{i}},0} \right)}}{{\sum\limits_{j = 1}^{n}I_{j}} + {\max \left( {{I_{j} - S_{j}},0} \right)}}.}}$

Thus, every job outcome of a solution has an opportunity score thatrepresents the current level of importance and satisfaction, and the sumof the values for each outcome is V (i.e., the value of the solution).The weighted value score is useful because it enables quantification ofthe advantage of satisfying a specific outcome relative to all otheroutcomes in a job. For example, if firefighters need to minimize thetime it takes to confirm receipt of a communication and they need tominimize the number of interruptions during a communication, it isuseful to know if satisfying the confirmation outcome will add morevalue than satisfying the interruptions outcome.

Quantifying the value, V, of competitive products relative to thebaseline value is useful because market share is rarely, if ever,distributed evenly. Experimentation with the techniques described inthis paper has yielded surprising accuracy in predicting that companieswith products that deliver value add of 20% or more relative to thebaseline value will capture a disproportionate share of the securablemarket, usually between 60% and 80%. The number of solutions with valueadd over 20% determines the competitive intensity of the market.

Table 1 helps to illustrate how it is possible to calculate preciselyhow much value must be delivered (e.g., which outcomes need to besatisfied and by how much) in order to capture market share. In Table 1,C1, C2, and C3 represent competing products.

TABLE 1 Value Add and Competition Importance Satisfaction OpportunityWeight Value C1 C2 C3 Outcome 1 8.8 4.4 13.2 40% 1.7 1.7 2.1 2.2 Outcome2 6.1 7.4 6.1 18% 1.4 2.1 1.9 1.9 Outcome n 9.0 4.1 13.9 42% 1.7 2.0 2.12.2 Total 33.2 100% 4.8 5.8 6.1 6.3 Value Add 20% 25% 30%

Since the size of the securable market, the value add of competitiveofferings, and the competitive intensity of the market are known, it ispossible to project the potential revenue for a new product with a highdegree of accuracy even before investment in product development.

In the example of FIG. 3, the flowchart 300 ends at module 312 withproviding a growth investment data structure. In a specificimplementation, the module 312 could be accomplished by a growthinvestment engine, such as is described with reference to FIG. 1. Byrevealing precisely where the market is underserved, securable marketanalysis enables management teams to make better investment decisionsand focus product development and R&D efforts on the right target. Thegrowth investment data structure could be provided by having a humanuser select it from among the target solution data structures, itsselection could be automated, or its selection could be partlyautomated. Providing the growth investment data structure will typicallyinclude sending the growth investment data structure to a team forfurther analysis, product development, or the like. Advantageously, thegrowth investment data structure can be provided before any investmentin research and development begins, and since the market is securable,predicted revenue for the growth investment can be determined withrelative precision.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise in this paper, discussions utilizing termssuch as “processing,” “computing,” “calculating,” “determining,”“displaying,” “generating,” “identifying,” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system's registersand memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

Data structures are data that has been stored in a format that may varyin an implementation-specific manner, but that includes characteristicsand structure necessary to accomplish the intended function. With thisin mind, FIGS. 6, 7, and 8 are intended to respectively illustrate a jobdata structure, an outcome data structure, and a solution datastructure.

In the example of FIG. 6, the job data structure 600 includes a job map602, emotional jobs link 604, consumption jobs link 606, job executorslink 608, and a gross WTP field 610. The job map 602 identifies outcomesthat are associated with the job. The job map can be broken into steps,each of which includes outcomes, or the job map can include outcomeswithout steps. The exact structure of the job map 602 isimplementation-specific, but could include, for example, an array oflinks to outcome data structures, an array of identifiers of outcomedata structures, or some other applicable convenient structure.

The emotional jobs link 604 identifies related emotional jobs, if any.The exact structure of the emotional jobs link 604 isimplementation-specific, but could include, for example, an array oflinks to emotional job data structures, an array of identifiers ofemotional job data structures, or some other applicable convenientstructure. Emotional jobs are sometimes amenable to being stored asoutcomes, but emotional jobs frequently have no outcomes associated withthem. As such, emotional jobs could be added as outcomes to the job map602 or treated as something a bit different than an outcome. Forexample, instead of treating an emotional job as an outcome, theemotional job could be correlated with a functional job that, whenaccomplished or when accomplished in a certain way, increases howsuccessful the emotional job can be considered to have beenaccomplished.

The consumption jobs link 606 identifies related consumption jobs, ifany. The exact structure of the consumption jobs link 606 isimplementation-specific, but could include, for example, an array oflinks to consumption job data structures, an array of identifiers ofconsumption job data structures, or some other applicable convenientstructure. Consumption jobs are sometimes amenable to being stored asoutcomes, since consumption jobs frequently have one outcome associatedwith them. As such, consumption jobs could be added as outcomes to thejob map 602. Consumption jobs frequently have a resource associated withthem, such as cost (e.g., setup costs, installation costs, trainingcosts, switching costs, etc.) or time. The various aspects of “switchingcosts” could be represented in a single consumption job data structure(either as an aggregated field or as one or more fields), or representedin separate consumption job data structures.

The job executors link 608 identifies job executors of the job, if any.The exact structure of the job executors link 608 isimplementation-specific, but could include, for example, an array oflinks to job executor data structures, an array of identifiers of jobexecutor data structures, or some other applicable convenient structure.Job executors can also be represented as a number (i.e., the number ofjob executors), as groups (e.g., grouping the job executors by thesolutions the job executors use to accomplish a job), or as individuals.Increasing granularity increases the ability to represent ranges ofvalues for different job executors (e.g., along the need curve).

The solutions link 610 identifies market solutions to accomplish thejob, if any. The exact structure of the solutions link 610 isimplementation-specific, but could include, for example, an array oflinks to solution data structures, an array of identifiers of solutiondata structures, or some other applicable convenient structure. Ifuseful, instead of a link, the job data structure 600 could include oneor more fields associated with solutions, such as value, number of jobexecutors using the solution, or the like.

The gross WTP field 612 identifies WTP for a solution that results inperfect satisfaction. The gross WTP field 612 is optional, and couldinstead be derived from WTP for specific job executors. The gross WTPfield 612 can serve as a baseline for the highest WTP that could beexpected for a solution to the job.

In the example of FIG. 7, the outcome data structure 700 includes a joblink 702, a vector of improvement 704, an object of control field 706,an importance field 708, and a gross WTP field 710. The job link 702provides a link to the job data structure associated with the job ofwhich the outcome is associated. The job link 702 may be obviated insome implementations, such as if the job data structure is always usedto link to the outcome data structure and no back-reference is needed.The vector of improvement 704 can include a metric (e.g., time,likelihood, number, etc.) and direction of improvement (e.g., minimize,increase, etc.). The exact structure of the vector of improvement 704 isimplementation-specific. The object of control field 706 identifies theobject that is being improved by the vector of improvement. Theimportance field 708 includes a value representative of the importanceof this outcome for an associated job. The importance field 708 couldinclude a range, and each value of the range could be associated with ajob executor. However, the granularity and amount of data provided byway of the importance field 708 is implementation-specific. Theimportance field 708 could be omitted in a specific data structure ifimportance were tracked on a solution-by-solution basis (or on a jobexecutor-by-job executor basis), but conceptually the importance maystill be considered part of the outcome data structure albeitindirectly. The gross WTP field 710 identifies WTP for a solution thatresults in perfect satisfaction with respect to this outcome. The grossWTP field 710 is optional and could be omitted in favor of WTP that isassociated with solutions to the job, rather than WTP that is associatedwith each outcome of a job.

In the example of FIG. 8, the solution data structure 800 includes a joblink 802, a satisfaction field 804, a job executor link 806, a net WTPfield 808, and cost field 810. The job link 802 provides a link to thejob data structure associated with the job with which the solution isassociated. The job link 802 may be obviated in some implementations,such as if the job data structure is always used to link to the solutiondata structure and no back-reference is needed. The satisfaction array804 includes a value representative of the satisfaction the solutionprovides to job executors for each outcome. The satisfaction array 804could include ranges, and each value of the ranges could be associatedwith a job executor. Instead of or in addition to the satisfaction array804, the solution data structure 800 could include links to each outcomedata structure of a job. The job executor links 806 identify the jobexecutors who use the solution to accomplish the job. The net WTP field808 identifies WTP for the solution, and may or may not includeindividual net WTP. The cost field 810 can include costs of using thesolution, including time, money, or other resources, and can be brokendown into convenient sub-fields in some implementations. The cost fieldcould instead or in addition be represented as a consumption jobsolution.

In each of the examples of FIGS. 6-8, the fields are provided by way ofexample and could be modified to meet the needs of a particularimplementation, broken into substructures that form the indicated datastructure in the aggregate, aggregated into larger data structures, orthe like. FIG. 9 depicts an example of a job data structure 900 withsubstructures.

In the example of FIG. 9, the job data structure 900 includes a surveysubstructure 901, a job substructure 902, a job scoring substructure904, an outcome substructure 906, an outcome scoring substructure 908,and a market segment substructure 910. In the example of FIG. 1, thesurvey substructure 901 includes a survey description field 911. Inoperation, the survey substructure 901 can include multiple other fieldsand/or substructures (not shown), such as demographics, respondents,survey question, market, company, etc. fields and/or substructures.

In the example of FIG. 9, the job substructure 902 includes a jobcategory 912, a platform type 914, a survey 916, a job description 918,a job type 920, a theme 922, and a growth path 924. In operation, thejob substructure 902 can include multiple other fields and/orsubstructures (not shown), such as subjobs (e.g., consumption oremotional jobs, a linked list of job steps, etc.). The job category 912can include a field or substructure that includes, for example, a jobcategory description, a survey id, or other fields and/or substructures.The job substructure 902 can be one of several jobs that are in the jobcategory associated with the job category 912. The platform type 914 caninclude a field or substructure that includes, for example, a platformtype description. A platform type can include rules and/or data that isassociated with a platform for launching a product to solve the job. Inthe example of FIG. 9, the survey 916 points to the survey substructure901, which was described previously. There can be multiple surveys thatare associated with the job with which the job substructure 902 isassociated. The job description 918 can include a description of the jobwith which the job substructure 902 is associated. The job type 920 caninclude field or substructure that includes, for example, job typedescription. In this example, the job type is intended to illustratethat jobs can be organized in a manner that is convenient for the usersof the job structure 900. The theme 922 can include a field orsubstructure that includes, for example, a theme description, a surveyid, etc. The theme can include a job map that includes multiple jobsteps and/or outcomes. The growth path 924 can include a field orsubstructure that includes, for example, a growth path description, asurvey id, or other fields and/or substructures. The growth path can beindicative of the type of growth investment that is appropriate for ajob, such as a premium growth path or a disruptive growth path.

In the example of FIG. 9, the job scoring substructure 904 includes ajob 926, a competitor 928, a market segment 930, an importance 932, asatisfaction 934, an opportunity 936, and a sample size 938. There canbe multiple job scoring substructures 904 for each job substructure 902.In the example of FIG. 9, the job 926 points to the job substructure902, which was described previously. The competitor 928 can include adescription of a competing solution for the job with which the job 926is associated. In the example of FIG. 9, the market segment 930 pointsto the market segment substructure 910, which will be described later.The importance 932 can include a value indicative of the importance ofthe job 926. The satisfaction 934 can include a value indicative of thesatisfaction associated with the competitor 928 that provides a solutionto the job 926. The opportunity 936 can include a value indicative of apotential growth investment for a target solution that replaces orcompetes with a current market solution. Opportunity can be representedas a function of importance and satisfaction, where there is generallygreater opportunity where importance is relatively high and satisfactionis relatively low. The sample size 938 can be indicative of the amountof data that was used to score the job 926.

In the example of FIG. 9, the outcome substructure 906 includes a job940, a platform type 942, a survey 944, an outcome description 946, atheme 948, and a growth path 950. There can be multiple outcomesubstructures 906 for each job substructure 902. In the example of FIG.9, the job 940 points to the job substructure 902, which was describedpreviously. The platform type 942 can include a field or substructurethat includes, for example, a platform type description. A platform typecan include rules and/or data that is associated with a platform forincorporating into a product a solution to the outcome associated withthe outcome substructure 906. In the example of FIG. 9, the survey 944points to the survey substructure 901, which was described previously.There can be multiple surveys that are associated with the outcome withwhich the outcome substructure 906 is associated. The outcomedescription 946 can include a description of the outcome with which theoutcome substructure 906 is associated. The theme 948 can include afield or substructure that includes, for example, a theme description, asurvey id, etc. The theme can include a job map that includes theoutcome. The growth path 950 can include a field or substructure thatincludes, for example, a growth path description, a survey id, or otherfields and/or substructures. The growth path can be indicative of thetype of growth investment that is appropriate for a job incorporatingthe outcome, such as a premium growth path or a disruptive growth path.

In the example of FIG. 9, the outcome scoring substructure 908 includesan outcome 952, a competitor 954, a market segment 956, an importance958, a satisfaction 960, an opportunity 962, and a sample size 964.There can be multiple outcome scoring substructures 908 for each outcomesubstructure 906. In the example of FIG. 9, the outcome 952 points tothe outcome substructure 906, which was described previously. Thecompetitor 954 can include a description of a competing solution for thejob with which the outcome 952 is associated. In the example of FIG. 9,the market segment 956 points to the market segment substructure 910,which will be described later. The importance 958 can include a valueindicative of the importance of the outcome 952. The satisfaction 960can include a value indicative of the satisfaction associated with thecompetitor 954 that provides a solution to the outcome 952. Theopportunity 962 can include a value indicative of a potential growthinvestment for a target solution that replaces or competes with acurrent market solution. Opportunity can be represented as a function ofimportance and satisfaction, where there is generally greateropportunity where importance is relatively high and satisfaction isrelatively low. The sample size 964 can be indicative of the amount ofdata that was used to score the outcome 952.

In the example of FIG. 9, the market segment substructure 910 includes asurvey 966, a market segment type 968, a market segment description 970,and a number of respondents 972. There are generally multiple marketsegments associated with each job substructure 902 and outcomesubstructure 906. The survey 966 points to the survey substructure 901,which was described previously. The market segment type 968 is intendedto illustrate that there can be multiple segment types as would beconvenient to one making use of the job structure 900. The marketsegment description 970 can include a description of the market segment.The number of respondents 972 can include a value indicative of thenumber of respondents to the survey 966, and can include other dataassociated with respondents, such as respondent ids, respondent types,respondent roles, respondent status, respondent profiles, respondentprofiles, demographics, attitudinal segments, and the like.

In the example of FIG. 9, the job data structure 900 does notdistinguish between functional, emotional, and consumption jobs. Suchjobs could be represented in the same or different data structures. Forexample, in one embodiment, financial statements can be used instead ofsurvey data for consumption jobs (note that financial statements couldbe considered survey data, and are distinguished from survey data forthe sake of example only). Emotional and consumption jobs could becoupled to a job data structure much as the consumption job datastructure 906 is linked to the job data structure 902 in the example ofFIG. 9.

FIG. 10 depicts a flowchart 1000 of an example of a method foridentifying a securable market. In the example of FIG. 10, the flowchart1000 starts at module 1002 with multiplying a number of job executors byimportance and dissatisfaction levels to obtain a securable population.An appropriate formula that has been implemented in an engine for thepurpose of determining a “securable population” is p=j*i*(1−s), where pis the securable population, j is the number of job executors, 0≦i≦1 isthe importance of the job, and 0≦s≦1 is the satisfaction of a targetsolution. Note that for maximum importance i.e., i=1, and maximumdissatisfaction, i.e., s=0, the securable population is the same as thepopulation of job executors. This formula has proven accurate intransforming a value that is representative of a number of job executorsinto a value that is representative of a securable population of jobexecutors, which is believed to be more valuable in the context ofgrowth investments associated with the job executors.

In the example of FIG. 10, the flowchart 1000 continues to module 1004with multiplying job frequency by maximum WTP to obtain gross WTP, whichreflects the maximum price a job executor will pay to get a jobassociated with the job data structure done with perfect satisfaction.As such, maximum WTP can serve as an upper limit when determining theprice point for a target solution to the job.

Maximum WTP can be obtained using surveys, and will likely varydepending upon the respondent. The respondents can be divided intomarket segments as appropriate, which could vary in size from a singlerespondent (in which case the maximum WTP would be as accurate aspossible within the limitations of the survey) to all respondents (inwhich case the maximum WTP would be some combination of answers). Sinceit is rarely the case that all job executors can be surveyed, it isgenerally necessary to use other data, statistical computations, orother applicable known or convenient techniques to derive useful maximumWTP values to augment the survey data. In an alternative, maximum WTPcan be estimated without the use of survey data, but accuracy is likelyto suffer.

It is worth noting that sometimes the “job frequency” is equal to thenumber of job executors for growth investment purposes. For example, ifa solution to the job “confirm receipt of a message” is being solved fora firefighter, the solution could be provided in the form of multi-usehardware that has no practical limitation on the number of times the jobis carried out. So the job frequency can be equal to the number of jobexecutors. In other cases, such as if the job “open an artery” is beingsolved for a cardiologist, the solution could be provided, perhaps inpart, in the form of a single-use stent. So the job frequency can besome multiple of the job executors. In this paper, it is assumed thatthe “job executor” could represent actual job executors or somemultiple, depending upon what is useful in a given context.

In the example of FIG. 10, the flowchart 1000 continues to module 1006with multiplying gross WTP by the satisfaction with a solution to obtainnet WTP. The net WTP can be associated with zero or more solutions(e.g., products or services). It is possible to have zero solutions fora job that does not yet have a market solution, in which case net WTPcould be set to ‘0.’ Where market solutions exist, surveys and/or othertechniques can be used to determine satisfaction with the marketsolutions.

In the example of FIG. 10, the flowchart 1000 continues to module 1008with quantifying emotional job WTP. Emotional jobs take into account,for example, the value add that a job executor will recognize forsolutions that have non-functional advantages.

In the example of FIG. 10, the flowchart 1000 continues to module 1010with quantifying consumption job WTP. Consumption jobs can include thecost of getting the job done with the relevant solution. Consumptionjobs are relatively straight-forward in the sense that an enterprise mayexplicitly keep track of costs for performing jobs.

In the example of FIG. 10, the flowchart 1000 continues to module 1012with calculating job premiums for the securable population, whichrepresent WTP for satisfaction at a higher level than a current solutioncan deliver. Thus, the job premium will typically have a value betweenthe net WTP of a market solution (if any) and the gross WTP for a targetsolution. The job premium is important for premium market analysis andis generally bounded by the gross WTP at the high end and the net WTP atthe low end for a securable population. There can be multiple jobpremiums in comparison with multiple market solutions, or it may beconvenient to compare the target solution with the highest-WTP marketsolution. For securable market disruption, the job premium can be lowerthan the net WTP.

In the example of FIG. 10, the flowchart 1000 continues to module 1014with calculating emotional job premiums for the securable population,which represent WTP to have emotional jobs related to the job satisfiedbetter than a current solution can deliver. Emotional job premiums andfunctional job premiums can be added together to obtain the total jobpremium, minus costs.

In the example of FIG. 10, the flowchart 1000 ends at module 1016 withcalculating consumption job premiums for the securable population, whichrepresent costs to get the job done, plus switching cost. As a generalrule, switching costs reduce consumption job premiums. For securablemarket disruption, consumption job premiums can be considered “moreimportant” than the total job premium, since the goal is to introducelower-cost market solutions that will facilitate accomplishment of a jobat a lower level of satisfaction than current market solutions (i.e.,the total job premium can be negative).

When the flowchart 1000 ends, a combined premium associated with atarget solution is available. By using the method repeatedly, it becomespossible to meaningfully compare multiple target solutions withdifferent outcome satisfaction levels. Conveniently, due at least inpart to the computation of the securable population, the combinedpremium has real predictive power regarding potential success of agrowth initiative, compared to more traditional market analysisquantifications/computations.

1. A system comprising: an outcome management engine; a securable marketinnovation engine coupled to the outcome management engine; a growthinvestment engine coupled to the securable market innovation engine;wherein, in operation, the outcome management engine generatespoint-in-time market solution-independent data structures, including ajob data structure and outcome data structures, wherein the job datastructure is associated with a job and includes a job map havingoutcomes, and wherein the outcome data structures are associated withthe outcomes and have importance levels that are also associated withthe outcomes; the securable market innovation engine generates one ormore target solution data structures, including a target solution datastructure having satisfaction levels corresponding to each of theoutcomes, wherein the satisfaction levels quantify satisfaction with atarget solution associated with the target solution data structure withrespect to each of the outcomes; the securable market innovation engineidentifies a securable market using the point-in-timesolution-independent data structures and a function of the importancelevels and satisfaction levels; the growth investment engine provides agrowth investment data structure associated with a growth investmenttargeting the securable market, wherein the growth investment datastructure is associated with the target solution.
 2. The system of claim1 further comprising a market quantification engine coupled to thesecurable market innovation engine, wherein, in operation, the marketquantification engine generates market solution-dependent datastructures, including a solution data structure having satisfactionlevels corresponding to each of the outcomes, wherein the satisfactionlevels quantify satisfaction with a solution associated with thesolution data structure with respect to each of the outcomes.
 3. Thesystem of claim 1, wherein identifying the securable market includes thesecurable market innovation engine multiplying a number of job executorsby importance and satisfaction levels to obtain a securable population.4. The system of claim 1 wherein identifying the securable marketincludes the securable market innovation engine calculating grosswillingness-to-pay (WTP), which reflects a job executor's WTP to get thejob associated with the job data structure done with perfectsatisfaction and multiplying gross WTP by the satisfaction with thetarget solution to obtain net WTP.
 5. The system of claim 1 whereinidentifying the securable market includes the securable marketinnovation engine calculating job premiums, which representwillingness-to-pay for satisfaction at a higher level than a currentsolution can deliver.
 6. The system of claim 1 wherein identifying thesecurable market includes the securable market innovation enginecalculating emotional job premiums, which represent willingness-to-payto have emotional jobs related to the job satisfied better than acurrent solution can deliver.
 7. The system of claim 1 whereinidentifying the securable market includes the securable marketinnovation engine calculating a total cost of getting the job done, plusswitching costs.
 8. The system of claim 1 wherein, in operation, thesecurable market innovation engine determines expected market share forthe securable market.
 9. The system of claim 1 wherein the function ofthe importance levels and satisfaction levels can be used to obtain anopportunity score equal to importance level plus the maximum of theimportance level minus the satisfaction level and zero.
 10. A systemcomprising: means for generating point-in-time marketsolution-independent data structures that at least include importance ofan outcome of a job; means for generating market solution-dependent datastructures that at least include satisfaction with an outcome of a jobfor a market solution; means for identifying a securable market usingthe point-in-time market solution-independent data structures and themarket solution-dependent data structures; means for generating targetsolution data structures, including a target solution data structureassociated with a target solution that at least includes satisfactionwith an outcome of a job for the target solution; means for determiningexpected market share for the target solution in the securable market;means for providing a growth investment data structure.
 11. The systemof claim 10, wherein the means for identifying the securable marketincludes a means for multiplying a number of job executors by importanceand satisfaction levels to obtain a securable population.
 12. The systemof claim 10 wherein the means for identifying the securable marketincludes a means for calculating gross willingness-to-pay (WTP), whichreflects a job executor's WTP to get the job associated with the jobdata structure done with perfect satisfaction and multiplying gross WTPby the satisfaction with the target solution to obtain net WTP.
 13. Thesystem of claim 10 wherein the means for identifying the securablemarket includes a means for calculating job premiums, which representwillingness-to-pay for satisfaction at a higher level than a currentsolution can deliver.
 14. The system of claim 10 wherein the means foridentifying the securable market includes a means for calculatingemotional job premiums, which represent willingness-to-pay to haveemotional jobs related to the job satisfied better than a currentsolution can deliver.
 15. The system of claim 10 wherein the means foridentifying the securable market includes a means for calculating atotal cost of getting the job done, plus switching costs.
 16. A methodcomprising: generating point-in-time market solution-independent datastructures that at least include importance of an outcome of a job;generating market solution-dependent data structures that at leastinclude satisfaction with an outcome of a job for a market solution;identifying a securable market using the point-in-time marketsolution-independent data structures and the market solution-dependentdata structures; generating target solution data structures, including atarget solution data structure associated with a target solution that atleast includes satisfaction with an outcome of a job for the targetsolution; determining expected market share for the target solution inthe securable market; providing a growth investment data structure. 17.The method of claim 16, wherein identifying the securable marketincludes multiplying a number of job executors by importance andsatisfaction levels to obtain a securable population.
 18. The method ofclaim 16 wherein identifying the securable market includes calculatinggross willingness-to-pay (WTP), which reflects a job executor's WTP toget the job associated with the job data structure done with perfectsatisfaction, and multiplying gross WTP by the satisfaction with thetarget solution to obtain net WTP.
 19. The method of claim 16 whereinidentifying the securable market includes calculating job premiums,which represent willingness-to-pay for satisfaction at a higher levelthan a current solution can deliver.
 20. The method of claim 16 whereinidentifying the securable market includes calculating emotional jobpremiums, which represent willingness-to-pay to have emotional jobsrelated to the job satisfied better than a current solution can deliver.21. The method of claim 16 wherein identifying the securable marketincludes calculating a total cost of getting the job done, plusswitching costs.